As physicians, insurance companies, hospitals, and other healthcare facilities identify areas for potential improvement in resource utilization and quality of care, the healthcare providers have a need for applicable industry benchmarks to determine the scale of improvement that might be reasonable to achieve.
New tools may now identify pockets (i.e., specific patient cohorts) of under-performance in healthcare at a level of granularity that healthcare providers have not previously seen. Therefore, there are often no publicly available benchmarks for how the industry as a whole performs for these specific cohorts of patients. There is a longstanding frustrated need to automatically measure related healthcare factors.
There is a longstanding frustrated need to have programmable hardware function in a better manner, and particularly to have hardware be smarter and learn from its own experience, with machine learning and artificial intelligence.
The longstanding frustrated need is particularly acute in the healthcare field. For example, in healthcare there is a need for computer architecture to accurately forecast aspects of healthcare to provide practical applications for healthcare providers. Systems that generate such forecasts automatically without human intervention would, of course, operate more efficiently, reliably, and faster than systems requiring constant human input to make such practical applications.